首个阴道超声子宫疤痕缺陷分割数据集，助力精准诊断。
Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark
- 构建1111张图像+16段视频的标注数据集
- 501个确诊样本含像素级人工标注
- 适合医学影像算法与妇产科临床研究
剖宫产术后子宫疤痕缺陷（CSD）是常见并发症，经阴道超声是主要筛查手段。准确识别CSD轮廓与尺寸对治疗至关重要。然而，由于病灶小、形态不规则、图像质量不佳及资源受限地区临床认知不足，常被漏诊。尽管人工智能在医学影像领域取得进展，但尚无公开的阴道超声CSD分割数据集。为此，本文构建了包含1,111张图像和16段视频的完整数据集，共501个阳性样本，均依据标准化临床指南，由经验丰富的超声医师与训练有素的博士生完成像素级人工标注。该数据集为推动医学图像分割算法发展和促进临床创新提供高质量基准资源。提升CSD诊断水平有助于改善育龄女性生活质量，具有重要科研与临床价值。
原文摘要 · Abstract (English)
Cesarean Scar Defect (CSD) is one of the most prevalent complications following cesarean delivery. Transvaginal ultrasonography is widely used for primary CSD screening. Accurate determination of CSD outline and dimensions is crucial for treatment. However, CSDs are frequently overlooked by sonographers due to small size and irregular morphology, suboptimal image quality, and limited clinical awareness in resource-constrained settings. Despite artificial intelligence advances in medical imaging, no public dataset exists for transvaginal ultrasound CSD segmentation. To address this gap, we present a comprehensive CSD dataset comprising 1,111 images and 16 videos, yielding 501 positive samples with confirmed CSD and precise pixel-level manual annotations. Annotations are performed following standardized clinical guidelines through collaboration between experienced sonographers and trained PhD students. This work provides high-quality benchmark resources for advancing medical image segmentation algorithms and promoting clinical innovation. Ultimately, improved CSD diagnosis and subsequent treatment strategies can enhance the quality of life in women of reproductive age, representing significant value for both medical research and clinical practice.
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